5 Best Facebook Scrapers in 2026 & Python Tutorial
Using Python and a managed Facebook scraping API lets you collect public posts, comments, likes, and shares. This tutorial demonstrates how to scrape Facebook posts by keyword and retrieve their URLs via Google search.
Then it explains how to extract detailed post data using the API, along with tips for scaling the process with tools like Apify, Nimble, and Crawlbase.
Pricing of the best Facebook scraper tools 2026
See the best Facebook scraping tools based on supported page types, output formats, pricing, and trial options.
* Starting price per month
- Dedicated: Returns structured JSON with key data fields from Facebook pages. These APIs are specifically designed for Facebook and provide higher accuracy.
- General-purpose: Not Facebook-specific but can be adapted for Facebook data scraping through custom parsing.
- NDJSON & JSONL: Uses newline-delimited JSON for efficient storage and processing of large datasets, with each line representing one JSON object.
Before examining the top tools below, the easiest way to understand how these APIs handle Facebook scraping is by seeing their output. You can download sample output from all providers.
Get samples from all vendors
Features of the best Facebook scraping tools
Bright Data Facebook scraper covers 15 dedicated templates for extracting public data from Facebook Pages, Profiles, Groups, Marketplace, Events, Reels, and Comments. Users can choose between two collection modes:
- Scraper API: allows developers to automate large-scale Facebook data scraping with scheduling, storage, delivery, and integration options.
- No-code scraper: a plug-and-play interface for non-developers to collect data directly from Facebook URLs through a control panel.
In addition to scraping live data, Bright Data also provides ready-to-use Facebook datasets (including posts, comments, marketplace listings, events, and profiles).
Pricing: Starts at $500/month (residential proxies included) with 20 free API calls.
Apify Facebook scraper can output data in JSON, CSV, or Excel. Inputs for the scraper must be Facebook Page URLs, which can be added manually, uploaded as a list, or provided via API.
The Facebook profile scraper can extract detailed information, such as page addresses, emails, and phone numbers, from the “About” section, even when this data isn’t available in the intro widget. Social media links are grouped by platform, and additional data is collected from the updated “About” and “Page Transparency” sections.
Pricing: The Starter plan, which costs $39 per month, reduces the scraping rate to around $10 per 1,000 pages and includes up to 3,900 pages per month. On the Free plan, you can scrape up to 500 pages.
Nimbleway offers a general-purpose scraping API adaptable to Facebook. It’s not specifically tailored for the platform, but it performs well for lightweight HTML-to-JSON scraping.
With the Facebook data scraper, you can target down to specific states and cities. They offer pay-as-you-go and monthly plans.
Pricing: $150/month (scraper+residential proxy/data volume), free trial available.
ScrapingBot is an affordable Facebook scraping software that supports posts and profiles, ideal for startups or small data teams. It handles proxy rotation automatically and outputs clean JSON or HTML for simple integrations.
Pricing: €22/month, free trial available.
Crawlbase offers dedicated Facebook scraping via its Crawling API, enabling users to collect structured JSON data from public Facebook pages, groups, profiles, events, and hashtags.
The API returns structured JSON that includes fields such as “title”, “type”, “membersCount”, “url”, and a “feeds” array containing post data like “userName”, “text”, “likesCount”, “commentsCount”, and “sharesCount”.
Pricing: $78/month
Facebook scraper Python tutorial
This step-by-step guide will show you how to scrape Facebook posts, scrape Facebook groups by keyword, fetch URLs via Google, and extract detailed post information using Bright Data’s Facebook post scraper.
How the Facebook scraper works
The Facebook scraper script is divided into four main steps:
- Setup & configuration: Import libraries, set up Python, and add API credentials.
- Find Facebook URLs: Use Google search to collect links for scraping Facebook posts.
- Trigger scraping: Send URLs to the Facebook data scraper API.
- Retrieve & save results: Download the scraped data and export it to a CSV file.
Step 1: Setup & configuration
Here, we import Python libraries for making HTTP requests, parsing data, and handling JSON. Add your API credentials from the dashboard and configure a proxy server for Google searches, essential for Facebook data scraping.
We then define our search parameters: looking for posts about “agentic frameworks” and collecting five posts (you can increase this number for deeper analysis using your Facebook scraper).
Step 2: Google Search for Facebook URLs
Now we search Google to find Facebook post URLs for Facebook data scraping.
This step performs the actual Facebook scraping setup using Google search. The script constructs a site:facebook.com query to locate relevant public posts, retrieves the HTML results, and extracts the post URLs (including shared posts and videos).
Duplicate links are filtered out, and a 2-second delay ensures that respectful, compliant requests are made to Google.
Step 3: Extracting post data
Next, we send the collected Facebook post URLs to the API for Facebook data scraping and extraction.
This step sends your Facebook URLs to the Facebook scraping API. Each URL is sent as JSON; if successful, the scraper returns a snapshot ID to track your scraping job. If the request fails, the script exits with an error message.
Step 4: Retrieve & save results
This step waits for the API to finish Facebook scraping and saves the collected data.
It extracts post details such as URL, username, date, likes, comments, and shares, then exports everything into a CSV file for analysis. The script includes timeout handling and error checks to keep your Facebook scraper reliable and efficient.
Is Facebook scraping legal?
Scraping Facebook is only legal when it involves collecting publicly available data and complies with Facebook’s Terms of Service. Facebook explicitly prohibits unauthorized data collection, automated scraping, and the access of private user information without consent.1
However, developers can still access certain types of Facebook data ethically and lawfully using official Facebook APIs. 2
What measures does Facebook take to prevent unauthorized scraping?
Facebook employs several anti-scraping measures to detect and block scraping attempts that violate its terms of service. These include:
- External Data Misuse (EDM) team: The External Data Misuse (EDM) team at Facebook is responsible for detecting potential data misuse and preventing unauthorized scrapers from violating Facebook’s policies and compromising user privacy.
- Rate limits refer to the number of times a user can interact with a website’s services within a given period. Facebook applies rate limits to prevent the overuse and abuse of its APIs.
- Request blocking through pattern recognition: Facebook employs algorithms to prevent automated Facebook scraping tools from overloading its systems. This technique involves analyzing the traffic and requests received by the server by using machine learning algorithms.
What is Facebook scraping?
Facebook scraping involves the automatic collection of publicly available data from Facebook pages, posts, profiles, or groups using code or specialized tools.
Scraping can be done with Python scripts or APIs, which simplify Facebook data scraping by automating proxy management.
💡Conclusion
There are several ways to access Facebook data safely, from using Facebook’s Graph API for approved applications to leveraging Facebook scraper APIs for public data or even running your own Python-based scraping scripts.
Building a Facebook scraper in Python allows you to automate Facebook data scraping safely and efficiently. Throughout this guide, you learned how to collect public Facebook post URLs and export results like post content, likes, comments, and shares into a clean CSV file for analysis.
By combining Python automation with a managed API, you can easily scale your Facebook post scraper to gather insights from any public page or topic.
If you need to expand your workflow, consider exploring tools such as Apify or Nimble, which offer dedicated Facebook page and profile scrapers.
FAQs about Facebook scrapers
Reference Links
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